HackGeriatrics

Inspiration

Both of us have experienced elderly falls. It is difficult when a senior is unable to call for help even with existing emergency button devices. We had a grandmother who recently had a fall a few months ago causing her to have a hemi-hip athroplasty (hip replacement surgery). After bringing her to rehab and meeting with her physicians and registered nurses, we learned of the staggering problem with unconscious falls among geriatric populations. We set out to develop a low-cost wearable to create an automated LifeAlert

What it does & How I built it

This application we built for Android and Android wear implements a fall detection algorithm using accelerometer, gyroscope, and magnetometer input. There are three axes for each MEMS device used; therefore, the fall sensitivity ratings are greatly improved and false positives are not as much of an issue. We were prompted to employ the support vector machine method to develop the algorithm. We collected my data by simulating falls myself with an Arduino 101 taped to my waist and chest and collecting from our own Huawei smartwatch

Challenges I ran into

Keeping false positives is a real issue. We are also having trouble publishing the application

Accomplishments that I'm proud of & What I learned

The SVM model really worked, and we are happy to have discovered so much from the large 9-axis dataset that was generated from all these sensors.

What's next for HackGeriatrics

We want to continue to develop around the machine learning algorithm that can determine falls with the best confidence ratings.